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What can an M5 Max with 64 GB run?

39 of 73 open models run well on it. The GPU can use about 48 GB of its 64 GB by default, and it reads memory at 614 GB/s, which sets how fast it writes. Found in: MacBook Pro (14-inch, M5 Pro or M5 Max), MacBook Pro (16-inch, M5 Pro or M5 Max), Mac Studio (M5 Max or M5 Ultra).

Smartest that runs wellQwen3.8 27B8-bit · 28.7 GB · 10–17 tok/sBiggest that runs wellNemotron 3.5 Bolt 74B A9B4-bit · 41.6 GB · 54–96 tok/sFor codeQwen3 Coder 30B A3B Instruct8-bit · 32.4 GB · 79–140 tok/s

Runs well 47

Fits with room for context, at 10 or more tokens a second.

ModelVersionMemoryTokens/s
Qwen3.8 27B27B · Intelligence 33.78-bit28.7 GB10–17
Nemotron 3.5 Bolt 74B A9B74B · 9B active · Intelligence 21.44-bit41.6 GB54–96
Qwen3.6 35B A3B35B · 3B active · Intelligence 18.28-bit37.2 GB86–154
Muse Glimmer 30B29.6B · Intelligence 17.56-bit24.1 GB11–20
Gemma 4 31B30.7B · Intelligence 14.76-bit24.9 GB11–20
Nemotron 3.5 Lightning 30B A3B30B · 3B active · Intelligence 12.98-bit31.9 GB86–154
Granite 4.2 30B30B · Intelligence 12.76-bit24.4 GB11–20
MiniCPM5 2B2.52B · Intelligence 12.58-bit2.7 GB102–182
Qwen3.5 9B9B · Intelligence 11.28-bit9.6 GB29–51
Granite 4.2 8B8B · Intelligence 11.18-bit8.5 GB33–58
Granite 4.2 3B3B · Intelligence 9.18-bit3.2 GB86–154
gpt-oss-20b21B · 3.6B active · Intelligence 9native 4.25-bit13.8 GB145–259
Nemotron 3 Nano 30B A3B30B · 3.5B active · Intelligence 8.98-bit31.9 GB75–133
Gemma 4 E4B8B · Intelligence 8.98-bit8.5 GB58–102
Ministral 3 8B8.9B · Intelligence 5.58-bit9.5 GB29–52
Nemotron 3 Nano 4B3.97B · Intelligence 4.88-bit4.2 GB66–117
Qwen3 Coder 30B A3B Instruct30.5B · 3.3B active8-bit32.4 GB79–140
GLM-4.7 Flash30B · 3B active8-bit31.9 GB86–154
Qwen3.6 27B27B8-bit28.7 GB10–17
Gemma 4 26B A4B25.2B · 3.8B active8-bit26.8 GB69–123
Devstral Small 2 (24B)24B8-bit25.5 GB11–19
Magistral Small 1.2 (2509)24B8-bit25.5 GB11–19
Mistral Small 3.2 (24B)24B8-bit25.5 GB11–19
LFM2.5 24B A2B24B · 2B active8-bit25.5 GB132–234
Phi-4 Reasoning Vision 15B15B8-bit15.9 GB17–31
Phi-4 (14B)14B8-bit14.9 GB19–33
Gemma 4 12B Unified12B8-bit12.7 GB22–39
Qwen3 8B8.2B8-bit8.7 GB32–56
Llama 3.1 8B Instruct8B8-bit8.5 GB33–58
Gemma 4 E2B5.1B8-bit5.4 GB115–205
Qwen3.5 4B4B8-bit4.3 GB64–114
Phi-4 mini instruct3.8B8-bit4 GB69–123
Llama 3.2 3B Instruct3.21B8-bit3.4 GB81–144
SmolLM3 3B3B8-bit3.2 GB86–154
LFM2.5 2.6B2.69B8-bit2.9 GB95–169
Qwen3.5 2B2B8-bit2.1 GB132–234
Llama 3.2 1B Instruct1.23B8-bit1.3 GB213–378
Qwen3.5 0.8B0.8B8-bit0.9 GB307–546
Qwen3 0.6B0.6B8-bit0.6 GB461–819
Qwen3.5 122B A10B122B · 10B active · Intelligence 15.62-bit lower quality38.1 GB89–158
gpt-oss-120b117B · 5.1B active · Intelligence 11.62-bit lower quality36.6 GB173–307
Mistral Small 4 (119B)119B · 6.5B active · Intelligence 11.32-bit lower quality37.2 GB138–246
Qwen3 Coder Next80B · 3B active · Intelligence 9.23-bit lower quality35 GB213–378
Llama 4 Scout 17B-16E Instruct109B · 17B active · Intelligence 6.52-bit lower quality34.1 GB52–93
Ornith 1.5 122B122B · 10B active2-bit lower quality38.1 GB89–158
Nemotron 3 Super 120B A12B120B · 12B active2-bit lower quality37.5 GB73–129
GLM-4.5 Air106B · 12B active2-bit lower quality33.1 GB73–129

Runs, but slowly 3

Fits, but under 10 tokens a second: fine for batch work, slow for chat.

ModelVersionMemoryTokens/s
Llama 3.3 70B Instruct70B4-bit39.4 GB7–12
Mistral Medium 3.5 (128B)128B · Intelligence 14.22-bit lower quality40 GB7–12
Devstral 2 (123B)123B2-bit lower quality38.4 GB7–13

Too big 23

Needs more memory than 64 GB, even at 2 bits.

Other Macs

Every Mac with 64 GB, from any chip.

The M5 Max with more or less memory:

Neighbouring chips:

Head to head:

Against PCs and graphics cards:

Compare it with any Mac or GPU · The M5 Max for local AI · The best Mac for local LLMs · Every Mac

Questions

What is the best LLM for an M5 Max with 64 GB?
Of the models with a published Intelligence Index, Qwen3.8 27B scores highest among those that run well here: 8-bit, about 10–17 tokens a second. The biggest that runs well is Nemotron 3.5 Bolt 74B A9B.
Can an M5 Max with 64 GB run Llama 3.3 70B?
Yes, but slowly: 4-bit, at about 7–12 tokens a second.
How fast is gpt-oss-20b on an M5 Max?
About 145–259 tokens a second (native 4.25-bit), from the chip’s 614 GB/s memory bandwidth.

Run them with MLX Harness (free, open source) or Apple’s mlx-lm in Terminal: step by step.

How it’s worked out

Memory: the model’s parameters at 8, 6, 4, 3 or 2 bits per weight plus MLX’s group scales (half a bit per weight), or its native format, with room for the context on top. By default macOS lets the GPU use about 75% of unified memory. Speed: memory bandwidth divided by the bytes read per token is the ceiling for one conversation; in llama.cpp’s public Apple Silicon benchmarks real runs reach about 45% to 80% of it, which is the range shown. These are estimates, not measurements: measure your own Mac on MLX Bench. Model figures from each official model card, chip figures from Apple’s Tech Specs. Checked 2026-10-19. Same data as mlx.app/compare.

What LLMs can an M5 Max with 64 GB run? Models and speed